A Digital-Simulation Model for a Full-Polarized Microwave Radiometer System and Its Calibration

A digital-correlation full-polarized microwave radiometer is an important passive remote sensor, as it can obtain the amplitude and phase information of an electromagnetic wave at the same time. It is widely used in the measurement of sea surface wind speed and direction. Its configuration is complicated, so the error analysis of the instrument is often difficult. This paper presents a full-polarized radiometer system model that can be used to analyze various errors, which include input signal models and a full-polarized radiometer (receiver) model. The input signal models are generated by WGN (white Gaussian noise), and the full-polarized radiometer model consists of an RF front-end model and digital back-end model. The calibration matrix is obtained by solving the overdetermined equations, and the output voltage is converted into Stokes brightness temperature through the calibration matrix. Then, we use the four Stokes parameters to analyze the sensitivity, linearity, and calibration residuals, from which the simulation model is validated. Finally, two examples of error analysis, including gain imbalance and quantization error, are given through a simulation model. In general, the simulation model proposed in this paper has good accuracy and can play an important role in the error analysis and pre-development of the fully polarized radiometer.

Interpreting sensory and cognitive signals in the cortical reading network

Receptive field properties measured in the reading portion of the ventral occipital-temporal (VOT) cortex are task- and stimulus-dependent. To understand these effects, we analyzed responses in visual field-maps (V1-3, hV4, VO1) whose signals are likely inputs to the VOT. Within these maps, each voxel contains neurons that are responsive to specific regions of the visual field; these regions can be quantified using the moving bar paradigm and population receptive field (pRF) analysis. We measured pRFs using several types of contrast patterns within the bar (English words, Hebrew words, checkers, and false fonts). Word and false-font stimuli produce estimates that are as much as 3-4 deg closer to the fovea than checker stimuli in all visual field maps, becoming very pronounced in V3, hV4 and VO-1. The responses in the visual field maps suggest that the pRF shifts depend mostly on the visual characteristics of the stimulus, and may be explained by sensory signal models and their known neural circuitry. Responses in the VOT reading regions do not follow the same pattern as the visual maps. The pRF centers are confined to the central five degrees, and the responses to false-fonts differ from the responses to words. To understand these VOT signals, we suggest it is necessary to extend the sensory pRF model to include an explicit cognitive signal that distinguishes words from false-fonts.

RECURSIVE EVALUATORS AND THEIR IMPLEMENTATION

The results of implementation of recursive evaluators are introduced in the present article. Realization of parameter estimation of different structural and signal models is essential in information systems.

Borehole Sonic Data Dispersion Analysis With a Modified Differential-Phase Semblance Method

Ruijia Wang, Richard Coates, and Jiajun Zhao The sonic wave fields produced by wireline and logging-while-drilling (LWD) monopole, dipole, and quadrupole tools often consist of multiple borehole modes. Classic frequency-slowness semblance-map methods used to process this data often detect only strongly excited modes and overlook weak ones, and erroneously detect some modes. Conventional dispersion processing methods can be separated into two groups: single-mode and multimode extraction algorithms. Single-mode methods are stable but only return one mode, the most energetic one, at each frequency. Single-mode methods include the differential-phase frequency-semblance (DPFS) method and the weighted spectral-semblance method. Multimode methods can return multiple modes at each frequency but may be unstable in some cases. Due to their assumptions about signal models, multimode methods are often sensitive to unbalanced receiver arrays, poor data quality, and formation heterogeneity. For example, in some extreme cases, such as a formation with strong heterogeneity, multimode methods may yield erroneous ghost modes or discontinuous dispersion curves for each mode. Borehole modes with different slowness have different arrival times. Converting the data to the frequency domain can obscure this critical information or encode these time differences into phase differences between adjacent frequencies. Conventional frequency-semblance approaches, which use only a single frequency independently from adjacent ones, ignore this phase information. In this paper, we propose employing the phase differences between adjacent frequencies to facilitate multimode dispersion analysis. We modify one conventional method to incorporate the arrival time of modes or the phase difference between adjacent frequencies. We validate the proposed approach with synthetic, laboratory, and field data. The results suggest the method can extract a much more comprehensive representation of modes present in the sonic data. Additionally, the method provides reliable estimates, even when the number of receivers is small. Unlike the Prony and matrix-pencil methods based on assumed signal models, the proposed approach, which we denote “Modified Differential-Phase Frequency Semblance” (MDPFS), is a modification of the single-mode differential phase approach. The MDPFS is still a semblance-based approach, and as with other semblance-based processing, it is expected to be less sensitive to unbalanced receiver arrays, poor data quality, and formation heterogeneity than other multimode algorithms.